Assessment and management system for rehabilitative conditions and related methods

ABSTRACT

Systems and methods are disclosed for the measurement of patient outcomes in a rehabilitation setting. In one exemplary method, an assessment relating to self-care is provided, an assessment relating to mobility is provided, and an assessment relating to cognition is provided, wherein the assessments have been pre-selected using item response theory.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the priority benefit of U.S. ProvisionalPatent Application 62/563,960, filed Sep. 27, 2017, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally rehabilitative techniques and,more particularly, to a computer-assisted method for assessing apatient.

BACKGROUND

An “outcomes measure,” also known as an “outcomes assessment tool,” is aseries of items used to determine varying medical conditions orfunctional status of a patient. One outcomes measure is the FunctionalIndependence Measure (FIM®), which provides a method of measuringfunctional status. The assessment contains eighteen items composed ofmotor tasks (13 items) and cognitive tasks (5 items). Tasks are rated bya clinician on a seven-point ordinal scale that ranges from totalassistance to complete independence. Scores range from 7 (lowest) to 91(highest) for motor skills and 7 to 35 for cognition skills. Itemsinclude eating, grooming, bathing, upper body dressing, lower bodydressing, toileting, bladder management, bowel management, bed to chairtransfer, toilet transfer, shower transfer, locomotion (ambulatory orwheelchair level), stairs, cognitive comprehension, expression, socialinteraction, problem solving, and memory.

The FIM measure uses a scoring criteria that ranges from a score of 1(which reflects total assistance) to a score of 7 (which reflectscomplete independence). A score of 7 is intended to reflect that apatient has complete independence. A score of 1 is intended to reflectthat a patient can perform less than 25% of the task or requires morethan one person to assist. As a result of this scoring system, manypatients who make improvements in a free-standing inpatientrehabilitation facility or an inpatient rehabilitation unit within ahospital do not necessarily register gains in their outcomes scoreduring their rehabilitation. For instance, a spinal cord injury patientmay make significant improvements to fine finger skill motor skillsduring rehabilitation, allowing the patient to use a computer or a smartphone. However, his or her FIM score in this situation would notimprove.

An outcomes measure is needed that more accurately captures assessmentof a patient's medical condition or functional status. Additionally, anoutcomes measure is needed that helps to better identify areas in whichpatients, such as rehabilitation patients, can improve.

An “item” is a question or other kind of assessment used in an outcomesmeasure. For example, one item on an outcomes measure known as the BergBalance Scale instructs a patient as follows: “Please stand up. Try notto use your hands for support.” A “rating” is a score outcome or otherevaluation in response to an item assessment. For example, the ratingsfor the Berg Balance Scale item are as follows: a rating of 4, whichreflects that the patient is able to stand without using her hands andstabilize independently; a rating of 3, which reflects that the patientis able to stand independently using her hands; a rating of 2, whichreflects that the patient is able to stand using her hands after severaltries; a rating of 1, which reflects that the patient needs minimal aidfrom another to stand or to stabilize; and a rating of 0, which reflectsthat the patient needs moderate or maximal assistance from another tostand.

Classical test theory is a body of related psychometric theory thatpredicts outcomes of educational assessment and psychological testingsuch as the difficulty of items or the ability of test-takers. It is atheory of testing based on the idea that a person's observed or obtainedscore on a test is the sum of a true score (error-free score) and anerror score. Classical test theory assumes that each person has a truescore, T, that would be obtained if there were no errors in measurement.A person's true score is defined as the expected number-correct scoreover an infinite number of independent administrations of the test.Unfortunately, test users never observe a person's true score, only anobserved score, X. It is assumed that observed score=true score plussome error, or X=T+E, where X is the observed score, T is the truescore, and E is the error. The reliability, i.e., the overallconsistency of a measure, of the observed test score X is defined as theratio of true score variance to the observed score variance. Because thevariance of the observed scores can be shown to equal the sum of thevariance of true scores and the variance of error scores, thisformulates a signal-to-noise ratio wherein reliability of test scoresbecomes higher as the proportion of error variance in the test scoresbecomes lower and vice versa. The reliability is equal to the proportionof the variance in the test scores that could be explained if the truescores were known. The square root of the reliability is the correlationbetween true and observed scores. Estimates of reliability can beobtained by various means, such as the parallel test or a measure ofinternal consistency known as Cronbach's coefficient α. Cronbach's α canbe shown to provide a lower bound for reliability, and thus, thereliability of test scores in a population is always higher than thevalue of Cronbach's α in that population.

SUMMARY

The problem of accurately measuring improvements in rehabilitationpatients is solved by developing an outcomes assessment thatincorporates factor analysis and item response theory.

The problem of measuring improvements in rehabilitation patients issolved by asking a series of questions to the patient and returning adomain-specific and/or composite score.

The problem of improving care in rehabilitation patients is solved bypredicting the domain-specific and/or composite score on an outcomesmeasurement of the patient and providing a clinical intervention if thedomain-specific and/or composite score falls below the predicted score.

DRAWINGS

While the appended claims set forth the features of the presenttechniques with particularity, these techniques, together with theirobjects and advantages, may be best understood from the followingdetailed description taken in conjunction with the accompanying drawingsof which:

FIG. 1 displays a flowchart for certain exemplary methods for preparinga preliminary outcomes measure;

FIG. 2 displays a flowchart for electronically collecting ratings forthe items in the preliminary outcomes measure;

FIG. 3 displays an exemplary scoring system of the IRT, comparing itagainst a FIM® score known in the prior art;

FIG. 4 displays an exemplary plot of certain data relating to patientscores in the Self Care, Cognition, and Mobility domains;

FIG. 5 further shows a patient's current and expected functional statuson each of the items/tasks in the Self Care domain;

FIG. 6A and FIG. 6B are an “FIM Explorer” section, with features toallow clinicians to select and/or set the goal for each FIM-specifictask;

FIG. 7 shows a comparison chart; and

FIG. 8 displays various plots for the Self Care domain in comparisonwith a patient's FIM score.

DETAILED DESCRIPTION

A “bifactor model” is a structural model wherein items cluster onto aspecific factors while at the same time loading onto a general factor.

The term “categorical” is used to describe response options for whichthere is no explicit or implied order or ranking.

A “Comparative Fit Index” (CFI) compares the performance of theconstructed structural model against the performance of a model thatpostulates no relationships between variables. A good-fitting modelgenerally has a CFI greater than 0.95.

A “complex structure” is a CFA structural model where at least one itemloads onto more than one factor.

“Confirmatory factor analysis” (CFA) is a form of factor analysisutilized where a psychometrician has an understanding of how the latenttraits and items should be grouped and related. A structural model isdeveloped, and this is fit to the data. A goal of the model is toachieve a good fit with the data.

A “constraint” is a restriction imposed on a model for the sake ofmathematical stability or the application of content area theory. Forexample, if two factors in a confirmatory factor analysis are notexpected to have any relationship between them, a constraint on thatcorrelation (requiring that it equal 0.00) can be added to the model.

A “continuous” variable is a variable that is measured withoutcategories, like time, height, weight, etc.

A “covariate” is a variable in a model that is not on a measure, but maystill have some explanatory power. For example, in rehabilitationresearch, it may occasionally be useful to include covariates for age,sex, length of stay, diagnostic group, etc.

“Dichotomous” describes response options that are ordinal with twocategories (e.g., low versus high). Alternatively, it may refer to itemsthat are scored correct vs. incorrect, which are conceptually alsoordinal responses with two categories.

“Differential item functioning” (DIF), in item response theory, is ameasure of how the parameter estimates may behave differently from groupto group (in different samples) or from observation to observation (overtime).

“Difficulty,” in item response theory, is the required minimum level ofa latent trait that is necessary to respond in a certain way. On ameasure with dichotomous responses, there is a single difficulty (e.g.,the minimum level of the latent trait that will raise the probability ofanswering correctly to 50% or greater). On a measure with polytomousresponses, “difficulty” is better described as “severity,” as there isnot usually a correct or incorrect answer. On a measure with polytomousresponses, the number of difficulties estimated is k−1, where k is thenumber of response options. These difficulties describe the level oflatent trait necessary to endorse the next highest category. Sometimesalso referred to as a threshold.

“Dimension” refers to the number of latent traits a measure addresses. Ameasure that records one trait is said to be unidimensional, while ameasure recording more than one trait is referred to asmultidimensional.

“Discrimination” is the ability of a test to differentiate betweenpeople with high versus low ability of the latent trait. Similarly, itdescribes the magnitude of the relationship between an item and a latenttrait. Conceptually, it is very similar to a factor loading andmathematically, it can be converted into a factor loading.

“Endorse” means to select a response option.

“Equality constraint” in item response theory and confirmatory factoranalysis, is a mathematical requirement to constrain the discriminationsor factor loadings to be equal when only two items load onto a factor.

“Equating” refers to the use of item response theory to drawsimilarities between the scores on different measures that record thelevel of the same latent trait(s). Equating may also be used to comparealternate forms of the same measure.

“Error” refers to a term to describe the amount of uncertaintysurrounding a model. A model with parameter estimates that are veryclose to the observed data will have low amounts of error, while onethat is quite different would have a large amount of error. Error mayalso indicate the amount of uncertainty surrounding a specific parameterestimate itself.

“Estimation” refers to the statistical process of deriving parameterestimates from the data. These procedures may be performed usingspecialized psychometric software known in the art.

“Exploratory factor analysis” is a form of factor analysis that clustersitems according to their correlations. This is often done without anydirection from the analyst other than how many factors should beextracted. The groupings are then “rotated.” Rotation methods attempt tofind factor loadings that are indicative of simple structure by makingsure that factor loadings are pushed towards −1.00, 0.00, or 1.00.

“Factor” in factor analysis describes a latent trait. Unlike a latenttrait in item response theory, factors do not normally have scoresassociated with them.

“Factor analysis” is a statistical method for determining the strengthand direction of the relationships between factors and items. The dataon which factor analysis is based are the correlations between items.Factor analysis can accommodate either ordinal or continuous data, butnot unordered categorical. It is possible to compute scores from factoranalysis, but IRT scores are more reliable. May either be exploratory orconfirmatory.

“Factor correlation” refers to a correlation between two factors. A CFAmodel with correlated factors is called “oblique.”

“Factor loading,” in factor analysis, describes the magnitude of therelationship between an item and a factor. It is not mathematically thesame as a correlation, though its scale and interpretation are similar.That is, values (usually) range from −1.00 to 1.00. A strong negativefactor loading indicates a strong inverse relationship between an itemand a latent trait, while a strong positive loading has the oppositeinterpretation. A factor loading of 0.00 indicates no relationshipwhatsoever.

“Fit statistics” or “fit index” refer to metrics used to quantify howwell the model performs. Popular fit metrics confirmatory factoranalysis and structural equation modeling include the root mean squareerror of approximation (RMSEA), the comparative fit index (CFI), theTucker-Lewis Index (TLI), and the weighted root mean-squareresidual/standardized root mean-square residual (WRMR/SRMR).

“General factor,” in a bifactor model, refers to the factor onto whichall items load.

“Graded response model” (GRM) is an extension of the two-parameterlogistic model that allows for ordinal responses. Instead of only onedifficulty, the graded response model yields k−1 difficulties, where kis the number of response categories.

“Hierarchical model” is a structural model where latent traits load ontoother latent traits, forming a hierarchy.

“Higher-/lower-order factor,” in a hierarchical model, is a higher-orderfactor is a type of latent variable onto which lower-order factors load.

“Index” is a term used to refer to a fit index/statistic (e.g.,comparative fit index) or as a synonym for “measure.”

“Item” refers to the questions, tasks, or ratings on a measure that areaddressed by a respondent or a respondent's representative (such as aclinician).

“Item characteristic curve (ICC)” is a graph that plots the probabilityof selecting different response options given the level of a latenttrait. Sometimes it also is called a “trace line.”

“Item response theory” (IRT) is a collection of statistical models usedto obtain scores and determine item behavior according to a structuralmodel. In one used form, IRT uses the response pattern of every personin the sample in order to get these item and score estimates. IRT usesdata that are ordinal or categorical. Mathematically speaking, itemresponse theory uses item and person characteristics in order to predictthe probability that a person selects a certain response option on agiven item.

“IRT score” is a score specific to IRT analysis that is given on astandardized scale. It is similar to a z-score. In one IRT scoringsystem, a score of 0.00 implies someone has an average level of a latenttrait, a large negative score implies a low level of the latent trait,and a large positive value implies a large level of a latent trait.

“Latent trait” is similar to a factor in factor analysis, but used moreoften in item response theory. A latent trait is what a related set ofitems purports to measure. It may be used interchangeably with factor,domain, or dimension.

“Latent variable” is a term for a variable that is not measureddirectly. It includes latent traits.

“Linking” is similar to equating, but for item parameter estimatesrather than scores.

“Load” is a verb used to describe what an item does on a factor. Forexample: “Item 4 loads onto the both the local dependence factor as wellas the general factor in this model.”

“Local dependence” (LD) is a violation of the local independenceassumption in which items are related for some reason other than thelatent trait. If local dependence appears to exist in the data, it canbe accounted for by either modeling a correlation between the items orby creating a local dependence factor. This can be due to a large numberof reasons, such as similar wording, nearly identical content, and thelocation of the items on a measure (this last example occurs frequentlyon the last items of a long measure).

“Local independence” refers to an assumption in psychometrics thatstates that the behavior of items is due to the latent traits in themodel and item-specific error and nothing else. When items violate thisassumption, they are said to be locally dependent.

“Manifest variable” is a generic term for a variable that is measureddirectly, and includes items, covariates, and other such variables.

“Measure” or “measurement” refers to a collection of items that attemptto measure the level of some latent trait. It may be usedinterchangeably with assessment, test, questionnaire, index, or scale.

“Model” in psychometrics is a combination of the response model and thestructural model. In general terms, it describes both the format of thedata and how the data recorded in the model's variables should berelated.

“Model fit” is a term used to describe how well a model describes thedata. This may be done in a variety of ways, such as comparing theobserved data against the predictions made by the model or comparing thechosen model against a null model (a model in which none of thevariables are related). The metrics used to assess model fit are calledfit statistics.

“Multidimensional” is a term used to describe a measure that recordsmore than one latent trait.

“Multigroup analysis” in IRT refers to the process by which the samplecan be split into different groups, and parameter estimates specific toeach group may be estimated.

“Nominal model” is similar to the graded response model, but for itemswith response options that are categorical rather than ordinal.

“Oblique” is an adjective used to describe factors that are correlated.

“Ordinal” describes the way an item records data. For example, possibleresponses to an item are a series of categories ordered from low to highor high to low.

“Orthogonal” describes factors that are restricted to a zerocorrelation.

“Parameter estimate” is a statistically-derived value estimated bypsychometric software. It is a generic term that may include things likeitem discriminations, factor loadings, or factor correlations.

“Path diagram” is a diagram meant to illustrate the relationshipsbetween items, latent traits, and covariates. In a path diagram,rectangles/squares represent observed variables (i.e., items,covariates, or any modeled variable for which there is explicitlyrecorded information), ovals/circles represent latent traits orvariables for which there is no explicitly recorded information,one-headed arrows reflect one-directional relationships (as in aregression), and two-headed arrows reflect correlation/covariancebetween modeled variables.

“Polytomous” is a term for items with more than one response option, andmay be either ordinal or categorical.

“Pseudobifactor model” is a bifactor model where not all items clusteronto specific factors. Instead, some items may only load onto thegeneral factor.

“Psychometrician” is a kind of statistician that specializes inmeasurement.

“Psychometrics” describes the statistics used in creating or describingmeasures.

“Rasch model” is a response model that hypothesizes that all itemdiscriminations are equal to 1.00. It usually is not used unless thisassumption is true or nearly true. This assumption eases interpretationof scores and difficulties and allows use of item response theory on(relatively) small sample sizes, but it is very uncommon that all itemdiscriminations behave identically. It is a simplified case of thetwo-parameter logistic model, which allows the item discriminations tovary. Because of this, the Rasch model is sometimes referred to as theone-parameter logistic model (1PL). It may be used when the responsesare dichotomous.

A “respondent” is someone who answers items on a measurement.

A “response” is a respondent's answer to an item.

“Response categories” are the different options a respondent may selectas a response to an item. If items yield dichotomous responses, the dataare recorded as either correct (1) or incorrect (0).

“Response model,” in item response theory, refers to the way ameasurement model handles the format of the responses. Popular responsemodels include the Rasch model, the two-parameter logistic model, thethree-parameter logistic model, the graded response model, and thenominal model.

A “response pattern” is a series of numbers representing a respondent'sanswers to each question on a measurement.

“Root mean square error of approximation” (RMSEA) is a fit statistic inapplied psychometrics. It measures the closeness of the expected data(the data that the model would produce) against the observed data. It isusually desirable that the RMSEA is below 0.08, though some of ordinaryskill in the art desire that the RMSEA be below 0.05.

A “score” is a numeric value meant to represent the level or amount ofthe latent trait a respondent possesses. Classical test theory computesscores as the sum of item responses, while item response theoryestimates these using both response patterns and item qualities.

“Sigmoid” (literally, “S-shaped”) is an adjective is occasionally usedto describe the shape of the TCC or the ICC of a 2PL item.

“Simple structure” is a structural model where all items load onto onefactor at a time.

“Specific factor,” in a bifactor model, is a factor onto which a set ofitems load.

“Structural equation modeling” (SEM) is an extension of confirmatoryfactor analysis (CFA) that allows relationships between latent variableslike latent traits. If all latent variables in the model are latenttraits, structural equation modeling (SEM) and CFA are often usedinterchangeably.

“Structural model” is a mathematical description that represents asystem of hypotheses regarding the relationships between latent traitsand items. It is depicted as a path diagram.

“Sum score” is a score computed by summing the numeric value of allresponses on a measure.

“Sum score conversion” (SSC) is a table that shows the relationshipbetween the sum scores and an IRT scores.

“Test characteristic curve” (TCC) is a figure that plots therelationship between sum scores and IRT scores.

“Testlet” is a small collection of items that measure some component ofthe overall latent trait. Creating a measure comprised of testlets canlead to more easily interpreted scores when the definition of the latenttrait is clearly defined beforehand.

“Threshold”: see “difficulty”.

“Tucker-Lewis Index” (TLI) is a fit index that compares the performanceof the constructed model against the performance of a model thatpostulates no relationships between the variables. A good fitting modelusually has a TLI of greater than 0.95.

“Three-parameter logistic model” (3PL) is an extension of thetwo-parameter logistic model that also includes a “guessing” parameter.For example, in a multiple-choice item with 4 choices, even guessingrandomly results in a 25% chance of answering correctly. The 3PL allowsfor this non-zero chance of answering correctly. It is used whenresponses are dichotomous.

“Trace line”: see “item characteristic curve.”

“Two-parameter logistic model” (2PL) is like the Rasch Model, but allowsitem discriminations to vary. It may be used when item responses aredichotomous.

“Unidimensional” is a term used to describe a measure that records onlyone latent trait.

“Variable” is a generic word used to describe a set of directly(manifest) or indirectly (latent) recorded data that measures a singlething.

“Weighted root mean-square error/standardized root mean-square error”(WRMR/SRMR) is a fit statistic that measures the magnitude of a model'sresiduals. Residuals are the differences between the observed data andthe data that the model predicts. The typical recommended WRMR value isbelow 1.00, though this recommendation may change based on size of thesample or complexity of the model. The WRMR is used when there is atleast one categorical variable in the model, while the SRMR is used whenall variables are continuous.

FIG. 1 displays a flowchart for certain exemplary methods for preparinga preliminary outcomes measure 100 for inclusion into an electronicmedical record.

In 101, an item set 200 is identified. In an embodiment, clinicians maybe queried to provide their input on appropriate items to include in theitem set 200, based on their training, education, and experience.Examples of clinicians may include physicians, physical therapists,occupational therapists, speech language pathologists, nurses, and PCTs.Items from the item set 200 may come from a variety of outcomes measuresknown in the art.

In 102, the items from the item set 200 may be grouped into one or moreof a plurality of areas, called “domains”, that are relevant to therapyor clinical outcomes. The clinicians may identify these domains. In anembodiment, items from the item set 200 may be grouped into threedomains, titled “Self-Care”, “Mobility”, and “Cognition”. It should beunderstood that other groupings of additional and/or alternative domainsare possible.

In 103, a related analysis step may occur. For instance, the frequencywith which an item in the item set 200 is used in traditional practiceto assess a patient in a medical setting may be analyzed. Alternately,the cost of equipment to conduct the item may be assessed. The clinicalliterature may be reviewed to identify the outcomes measures with itemsin the item set 200 that are psychometrically acceptable and clinicallyuseful. For instance, the reliability and validity of an outcomesmeasure with one or more items in the item set 200 may be reviewed toensure it is psychometrically acceptable. As another example, eachoutcomes measure and/or item may be reviewed to ensure it is clinicallyuseful. For example, while there are many items used to test a person'sbalance that are available in the literature, not all of them areappropriate for patients in a rehabilitation context. Based on these andsimilar factors, the initial set of items may be narrowed to reduce theburden to patients, clinicians, and other health care providers.

In 104, a revised plurality of items is collected. A pilot study may beconducted on the plurality of items. The pilot study may be conducted byhaving clinicians assess patients on the revised items in a standardizedfashion, such that each clinician assesses each patient using all of therevised items. In another embodiment, the clinicians may select whichitems should be used to assess a patient, based on the patient'sparticular clinical characteristics. The determination as to selectionof specific items may be made based on information received during thepatient's rehabilitation stay, for instance, at the inpatient evaluationat admission. The item may be administered at least twice during thepatient's inpatient stay in order to determine patient progress. Thepilot study may be facilitated using an electronic medical recordsystem, such that clinicians enter item scores into the electronicmedical record.

In 105, a pilot study analysis may be performed. For instance, itemsthat take too much time for a clinician to conduct with a patient may beremoved.

In 106, the original paper-based items for the preliminary outcomesmeasure 100 are implemented in an electronic medical record. Individualitem-level rating can be recorded electronically. For instance, theitems to be implemented may be the items that are the result of thepilot study analysis in 105. However, pilot study analysis is notrequired. Alternately, the items in the preliminary outcomes measure 100could be implemented in an electronic system, such as a database, thatis external to an electronic medical record. In one embodiment, theexternal electronic system may be in communication with the electronicmedical record, using methods that are known in the art, such asdatabase connection technologies. In 107, the items for the preliminaryoutcomes measure 100 are programmed into the EMR using known methods,allowing clinicians to input their ratings into the electronic medicalrecord. In an embodiment, the EMR may provide a prompt to alert, remind,and/or require the clinician to enter certain ratings for certain itemsof the preliminary outcomes measure 100. Such prompts may improve thereliability and completeness of clinician data entry into the EMR.

Although the discussion above with reference to FIG. 1 refers to theselection of certain items from various outcome measures, it should beunderstood that a similar method may be conducted with respect to theselection of the outcome measures themselves. For example, in 104,instead of selecting which items to be used in assessing a patient, theentire outcome measure could be selected or disregarded.

FIG. 2 displays a flowchart for electronically collecting ratings forthe items in the preliminary outcomes measure 100. In 201, a clinicianconducts an assessment on a patient. In one embodiment, the clinicianmay conduct the assessment using every item in the preliminary outcomesmeasure 100. In another embodiment, the clinician may conduct thosetests or items in the preliminary outcomes measure 100 that are specificto that clinician's scope of practice. For example, a physical therapistmay conduct those tests or item in the preliminary outcomes measure 100that are specific to physical therapy. In yet another embodiment, theclinician may use her clinical judgement, based on her education,training, and experience, to identify the tests in the preliminaryoutcomes measure 100 that are most relevant to the patient. If thepatient is very ill or has very limited functioning, the clinician willknow not to conduct certain items. For instance, a clinician would notask a newly quadriplegic patient to perform a test that would requirethe patient to walk.

The assessment may be an initial assessment conducted at or shortlyafter the time of admission of the patient to a hospital. In anembodiment, each patient receiving care during a period of time, such asa month or a year, is assessed. In another embodiment, a majority ofpatients receiving care during a period of time are assessed. In yetanother embodiment, a plurality of patients are assessed. In otherembodiments, the patient population may be refined to include onlyinpatients, only outpatients, or a combination thereof.

In various embodiments, certain tests in the preliminary outcomesmeasure 100 may be conducted once at or shortly after admission, andagain at or shortly prior to discharge. In various embodiments, certaintests in the preliminary outcomes measure 100 may be conducted weekly.In various embodiments, certain tests in the preliminary outcomesmeasure 100 may be conducted more than once per week, such as twice perweek.

In an embodiment, the assessments may be conducted in a centralizedlocation specific to conducting assessments. The assessments may beconducted by a set of clinicians whose specific function is to conductassessments. A centralized location with qualified staff and adequateequipment to objectively assess a patient's functional performance maybe conducted through a standardized process in a controlled and safeenvironment. In an embodiment, a clinician provides an order for a labtechnician assessment. For example, a clinician (such as a physiatrist,therapist, nurse, or psychologist) orders a specific test (such as atest of gait and balance) or a group of tests. The test order may besent electronically to the assessment department (“AAL”) and a hard copymay be printed for the patient. When the AAL is ready, the patient maytravel to the AAL, with assistance if necessary. Staff, such as atechnician, performs the ordered test(s). Test results may be recordedand entered/transmitted into the electronic medical record. Theclinician may review test results to modify care plan if necessary. Thisprocess can reduce the amount of time clinicians require to learn how toconduct a test. One benefit of an AAL is that other clinicians do notneed to learn how to conduct various tests every time a new test isintroduced. Clinicians will only need to learn how to read the testresults, not how to conduct the test. Qualified personnel with propertraining can perform the tests. Clinical staff can focus on treatmentrather than on assessment. More treatment sessions or additional timecan be provided to improve outcomes. Test equipment is centrally kept toreduce the need for multiple units and maintenance costs. Tests can beconducted in a well-controlled, standardized and safe environment. Thetechnician may utilize standardized procedures to avoid potential raterinduced bias (tendency for higher ratings to show improvement overtime), thus improving data quality.

The ratings from each assessment may be saved in the EMR. For instance,they may be saved in a preliminary ratings dataset 150. In 202, dataanalysis and cleanup may be performed on the preliminary ratings dataset150 to improve data quality. For example, out-of-range ratings may beremoved from the preliminary ratings dataset 150. Patterns of data inthe preliminary ratings dataset 150 from the same clinician may bereviewed and cleaned using methods known in the art. Ratings in thepreliminary ratings dataset 150 from patients that show a large increasein rating from “dependent” to “independent” may also be discarded.Suspect data from a particular evaluation may be discarded.

In 203, the ratings data may be further extracted, cleaned, and preparedusing methods known in the art to get the data in a form in which thedata may be queried and analyzed. Data may be reviewed for quality, andvarious data options, such as data pivoting, data merging, and creationof a data dictionary may be performed for the preliminary ratingsdataset 150. Data from the preliminary ratings dataset 150 may be storedin the EMR or in a different form, such as in a data warehouse, forfurther analysis. It will be understood by one of ordinary skill in theart that many ways exist to structure the data in the preliminaryratings dataset 150 for analysis. In one embodiment, the preliminaryratings dataset 150 is structured so that item ratings are available foranalysis across a plurality of dimensions, such as time period andpatient identification.

Once the preliminary ratings dataset 150 has been prepared for analysis,a psychometric evaluation may be performed on the preliminary ratingsdataset 150. A psychometric evaluation assesses how well an outcomesmeasure actually measures what it is intended to measure. A psychometricevaluation may include a combination of classical test theory analysis,factor analysis, and item response theory, and assesses the preliminaryratings dataset 150 for various aspects, which may include reliability,validity, responsiveness, dimensionality, item/test information,differential item functioning, and equating (score crosswalk). In oneembodiment, classical test theory analysis may be employed to review thereliability of the items in the preliminary outcomes measure 100, andhow the preliminary outcomes measure 100 and the domain work together.

Item Reduction.

The item reduction step 152 assists in reducing the items from thepreliminary outcomes measure 100 that do not work as anticipated.Factors can include reliability, validity, and responsiveness (alsoknown as sensitivity to change). The purpose of the item reduction step152 is to eliminate potential item content redundancy from items in thepreliminary outcomes measure 100 to a minimal subset of items in an IRToutcomes measure 180 without sacrificing the psychometric properties ofthe data set. The item reduction step 152 may be performed using acomputer or other computing device, for instance, using a computerprogram 125. The computer program 125 may be written in the Rprogramming language or another appropriate programming language. Thecomputer program 125 provides an option to allow the number of desireditems (as well as options to include specific items) to be specified andcomputes the Cronbach's coefficient α reliability estimate for everypossible combination of items within those user-defined constraints.Acceptable ranges of Cronbach's coefficient α may also be defined in thecomputer program 125. Additionally, the computer program 125 mayconstruct and run syntax for a statistical modeling program, such asMplus (Muthen & Muthen, Los Angeles, Calif., http://www.statmodel.com)to determine the fit of a 1-factor confirmatory factor analysis (CFA)model to each reduced subset 155 of items.

The computer program 125 may be used to analyze several of the outcomesmeasures included in the preliminary outcomes measure 100 (such as theFIST, BBS, FGA, ARAT, and MASA), and searched for unidimensional subsetsbetween four and eight items with Cronbach's α reliabilities between0.70 and 0.95. Using these constraints, the number of items in manymeasures may be reduced substantially. For instance, measures may bereduced by at least half of their original length while maintaining goodpsychometric properties. The resulting item subsets served as buildingblocks for the confirmatory factor analysis (CFA). In an embodiment,certain items may not be included in the item reduction process, such asthe items from the FIM®. In an embodiment, the item reduction step 152may performed multiple times. For instance, it may be performed on eachoutcomes measure included in the preliminary outcomes measure 100.

In the item reduction step 152, the computer program 125 determines theextent to which items are related to each other. The computer program125 may determine the extent to which items within an outcomes measurein the preliminary outcomes measure 100 are related to each other. Inone embodiment, items are related to each other within an outcomesmeasure if they have responses which correlate highly. The analysis maystart by providing an initial core set of items, the number of which maybe determined with clinician input, based on correlations between itempairs. For example, the computer program 125 may determine how item Arelates to item B, where item A and item B are both in the same outcomesmeasure. If there is a high correlation, both item A and item B areincluded in the core set. Then, the computer program 125 may determinehow new item C correlates to the set of items {A, B}. If there is a highcorrelation, item C is included in the core set. The method may berepeated with additional items, D, E, F, etc. As described above, theprogram assess the reliability (Cronbach's α) of every possible subsetof items. The program correlates the responses from one set of itemswith the responses from a second set of items. Chronbach's α is known inthe art but a brief example is hereby provided. The information used incomputing Cronbach's α are the correlations between every possiblesplit-half in the subset of items. For example, using 3 items {A, B, C},Cronbach's α averages the correlations A vs. BC, B vs. AC, and C vs. AB.In other words, the correlations are computed between every pair ofunique subsets of a set. The purpose of the correlational analysis is tohelp ensure that items are measuring the same underlying construct andimproving reliability.

Table 1 lists an exemplary output of the item reduction step 152 for theBerg Balance Scale (“BBS”) outcomes measure, setting the sample sizeequal to five items. The numbers in each cell in the “item” columnsreflect the number of the question on the BBS (1: sitting unsupported;2: change of position—sitting to standing; 3: change ofposition—standing to sitting; 4: transfers; 5: standing unsupported; 6:standing with eyes closed; 7: standing with feet together; 8: tandemstanding; 9: standing on one leg; 10: turning trunk (feet fixed)). Eachreduced subset 155 is shown along with its associated Cronbach's αvalue. The first reduced subset has the highest Cronbach's α of thereduced subsets in Table 1. In one embodiment, the reduced subset withthe highest Cronbach's α is used as the initial reduced subset for theCFA step 160, which is described below in further detail.

TABLE 1 Item Item Item Item Item Cronbach's Grouping 1 2 3 4 5 α 1 1 2 45 6 0.9594018 2 1 2 4 6 7 0.9592235 3 1 2 4 6 8 0.9584257 4 1 2 4 6 100.9583003 5 2 6 7 8 10 0.9552663 6 1 2 4 6 9 0.9550176 7 2 4 6 8 100.9549165 8 2 4 6 7 10 0.9547042 9 2 4 6 7 8 0.9539102 10 1 2 6 8 100.9538519

Confirmatory Factor Analysis.

Factor analysis is a statistical method that is used to determine thenumber of underlying dimensions contained in a set of observed variablesand to identify the subset of variables that corresponds to each of theunderlying dimensions. The underlying dimensions can be referred to ascontinuous latent variables or factors. The observed variables (alsoknown as items) are referred to as indicators. Confirmatory factoranalysis (CFA) can be used in situations where the dimensionality of aset of variables for a given population is already known because ofprevious research. CFA may be used to investigate whether theestablished dimensionality and factor-loading pattern fits a new samplefrom the same population. This is the “confirmatory” aspect of theanalysis. CFA may also be used to investigate whether the establisheddimensionality and factor-loading pattern fits a sample from a newpopulation. In addition, the factor model can be used to study thecharacteristics of individuals by examining factor variances andcovariances/correlations. Factor variances show the degree ofheterogeneity of a factor. Factor correlations show the strength ofassociation between factors.

Confirmatory factor analysis (CFA) may be performed using Mplus or otherstatistical software to validate how well the item composition withinthe pre-specified factor structure holds statistically. CFA ischaracterized by restrictions on factor loadings, factor variances, andfactor covariances/correlations. CFA requires at least m̂2 restrictionswhere m is the number of factors. CFA can include correlated residualsthat can be useful for representing the influence of minor factors onthe variables. A set of background variables can be included as part ofa CFA.

Mplus can estimate CFA models and CFA models with background variablesfor a single or multiple groups. Factor indicators for CFA models can becontinuous, censored, binary, ordered categorical (ordinal), counts, orcombinations of these variable types. When factor indicators are allcontinuous, Mplus has seven estimator choices: maximum likelihood (ML),maximum likelihood with robust standard errors and chi-square (MLR, MLF,MLM, MLMV), generalized least squares (GLS), and weighted least squares(WLS) also referred to as ADF. When at least one factor indicator isbinary or ordered categorical, Mplus has seven estimator choices:weighted least squares (WLS), robust weighted least squares (WLSM,WLSMV), maximum likelihood (ML), maximum likelihood with robust standarderrors and chi-square (MLR, MLF), and unweighted least squares (ULS).When at least one factor indicator is censored, unordered categorical,or a count, Mplus has six estimator choices: weighted least squares(WLS), robust weighted least squares (WLSM, WLSMV), maximum likelihood(ML), and maximum likelihood with robust standard errors and chi-square(MLR, MLF).

Using the highly-reliable subsets of items from the measure reductionstep, a model may be defined in statistical software such as Mplus thathypothesizes that all items within a domain are interrelated. The modelalso may measure specific constructs under the preview of that domain.For example, all item subsets taken from Self Care measures may behypothesized to measure Self Care, but also simultaneously measure oneof Balance, Upper Extremity Function, and Swallowing. Constructing themodel in this way allows for the measurement of both an overall domain(e.g., Self Care) as well as a set of interrelated constructs thatcompose that domain (e.g., Balance, UE Function, and Swallowing—theconstructs composing Self Care). Given the data, the structure of themodel implies a set of expected correlations between each pair of items.However, these (polychoric) correlations can be computed directly fromthe data. These are the observed correlations. The appropriateness ofthe constructed model, called “model fit” in statistics, may bedetermined using the root mean square error of approximation (RMSEA),which is a measure of the difference between the observed and expectedcorrelations. In a preferred embodiment, if the value of that differenceis low (for instance, less than 0.08) the model has acceptable fit.

After applying the CFA step 160 on a reduced subset 155, the output ofthe CFA step 160 may contain factor loadings, including a General Factorloading. The General Factor loading may be between −1 and 1, with valuesof the General Factor loading of between 0.2-0.7 indicating whether afactor assesses the relevant item well. The output of the CFA step 160may provide additional factor loadings for each item. In an embodiment,each item may have a factor loading for each sub-domain. For instance,each item may have a factor loading value for Balance, a factor loadingvalue for Upper Extremity, a factor loading value for Swallowing, and afactor loading value for each other sub-domain. Where the item isrelevant to a sub-domain, the factor loading value will be non-zero, inan embodiment.

In certain instances, applying the CFA step 160 on a reduced subset 155can create problems that require selection of a new reduced subset 155.For instance, a general factor loading value higher than 0.7, orparticularly a value closer to 1.0, indicates redundancy. For instance,the way items are scored on the Action Research Arms Test (ARAT)outcomes measure necessarily forces too high of a reliability. Patientswho achieve a maximum score on the first (most difficult) item arecredited with having scored 3 on all subsequent items on that scale. Ifthe patient scores less than 3 on the first item, then the second itemis assessed. This is the easiest item, and if patients score 0 then theyare unlikely to achieve a score above 0 for the remainder of the itemsand are credited with a zero for the other items. This method of scoringforces the too-high reliability. In other instances, if a factor loadingvalue is greater than 1, it reflects that a pair of items has a negativevariance (which is not possible) and so the CFA step 160 must be run ona new reduced subset 155. A new reduced subset 155 may be selected fromthe group of reduced subsets generated by item reduction step 152. Forinstance, new reduced subset may be selected that has the next-highestChronbach's α, then applying CFA step 160 to the new reduced subset.

Additionally, during the process of running the CFA step 160, it may beapparent that items designated by clinicians as falling within onesub-domain should be moved to a different sub-domain in order to improvethe fit of the model used to generate the IRT outcomes measure 180(discussed further below). For example, during the development of theembodiments described herein, items identified by clinicians as relatingto “Strength” were initially placed in the Self-Care domain. In runningthe CFA step 160, however, it was determined that these items did notfit the model. Moving these items to the “Upper Extremity Function”sub-domain improved the fit of the model.

Table 2 below shows the fit statistics of a 1-factor CFA containinggroupings 1-10 set out in Table 1. In the CFA step 160, an assessmentmay be conducted as to whether the fit statistics listed in Table B meetusual “good fit” criteria. In one embodiment, these criteria areRMSEA<0.08, CFI>0.95, TLI>0.95, and WRMR<1.00. Those of ordinary skillin the art will appreciate that other good fit criteria could be used.

TABLE 2 Grouping fac rmsea cfi tli wrmr Meets Criteria? 1 1 0.145 0.9990.997 0.016 No 2 1 0.155 0.998 0.997 0.017 No 3 1 0.143 0.999 0.9970.015 No 4 1 0.148 0.998 0.997 0.017 No 5 1 0.09 0.999 0.999 0.009 No 61 0.126 0.999 0.998 0.014 No 7 1 0.076 1 0.999 0.008 Yes 8 1 0 1 1 0.002Yes 9 1 0.028 1 1 0.004 Yes 10 1 0.09 0.999 0.999 0.009 No

Although the example above is given only with respect to one outcomesmeasure, the Berg Balance Scale, it should be understood that the CFAstep 160 is applied to each outcomes measure in the preliminary outcomesmeasure 100.

Item Response Theory. In an embodiment, the IRT outcomes measure 180 maybe structured to contain a plurality of high-level domains. For example,the IRT outcomes measure 180 may be structured to include a “Self Care”domain (which includes items determined to reflect a patient'scapability to perform self care), a “Mobility” domain (which includesitems determined to reflect a patient's capability to be mobile), and a“Cognition” domain (which includes items determined to reflect apatient's cognitive capabilities). Within each higher-level domain,specific assessment areas, also referred to as “factors” or “clusters,”may be identified. Table 3 reflects exemplary assessment areasassociated with each higher-level domain.

TABLE 3 Domain Areas/Clusters Self-Care Balance UE Function StrengthChanging Body Position Swallowing Mobility Balance W/C Skills ChangingBody Positions Bed Mobility Mobility Cognition Awareness AgitationMemory Speech Communication

Because the measurement goals of the IRT outcomes measure 180 involvedmeasuring general domains (i.e., Self Care, Mobility, and Cognition) aswell as specific assessment areas within those domains, a bifactorstructure for each of the domains may be targeted (the general factorand domain-specific factor). The composition of the specific factors maybe determined by the content of each item set. For example, items fromthe FIST, BBS, and FGA may be combined to form the “Balance” assessmentarea within the Self Care domain. Acceptable fit of the bifactor modelto the data was assessed using the criterion of RMSEA<0.08 (Browne &Cudeck, 1992), and modification indices were also computed to check forlocal item dependence and potential improvements to the model, such asadditional cross-loadings (in other words, an item contributes toseveral factors).

Item Response Theory reflects a mathematical model that describes therelationship between a person's ability and item characteristics (suchas the difficulty). For example, a more able person is more likely to beable to perform a harder task, and can allow a more tailoredintervention based on a series of questions. Other item characteristicsmay be relevant as well, such as an item's “discrimination,” which isits ability to distinguish between people with high or low levels of atrait.

After constructing the CFA models for each of the domains, the finalstructures may be coded to run in an item response theory softwarepackage, such as flexMIRT (Vector Psychometric Group, Chapel Hill, N.C.,US). flexMIRT is a multilevel, multidimensional, and multiple group itemresponse theory (IRT) software package for item analysis and testscoring. The multidimensional graded response model (M-GRM) may bechosen to account for the ordered, categorical nature of the itemresponses from the clinician-rated performance ratings. For example, thedimensions may be “Self-Care,” “Mobility,” and “Cognition”. Sub-domainsfor “Self-Care” may be “Balance, “Upper Extremity Function,” “Strength,”“Changing Body Position”, and “Swallowing.” Sub-domains for “Mobility”may be Balance, Wheelchair (“W/C”) Skills, Changing Body Positions, BedMobility, and Mobility. Sub-domains for “Cognition” may be “Awareness,”“Agitation,” “Memory,” “Speech,” and “Communication.”

In a preferred embodiment, however, sub-domains may be reduced in orderto focus on key subdomains of ability. For “Self-Care”, for example,these may be Balance, UE Function, and Swallowing. For “Cognition” thesemay be Cognition, Memory, and Communication. For “Mobility,” there maybe no sub-domains—in other words, the sub-domains may all be clusteredtogether.

The analysis also may be multigroup in nature. For example, the SelfCare and Mobility samples may be split into groups determined by thelevel of balance (sitting, standing, or walking). As another example,the Cognition sample may be split into broad diagnostic categories(stroke, brain injury, neurological, or not relevant). In an embodiment,in order to accommodate the complexity of the models, theMetropolis-Hastings Robbins-Monro (MH-RM) algorithm (Cai, 2010) may beused for more efficient parameter estimation. MH-RM cycles through thefollowing three steps repeatedly until the differences between twoconsecutive cycles are smaller than a chosen criterion. In Step 1(Imputation), random samples of the latent traits are imputed from adistribution implied by the item parameter estimates taken from thepreceding cycle. If it is the first cycle, then the distribution impliedby the algorithm's starting values are used. This imputation can beperformed using the MH sampler. In Step 2 (Approximation), thelog-likelihood of the imputed data is evaluated. In Step 3(Robbins-Monro Update), new parameter estimates for the next cycle arecomputed by using the Robbins-Monro filter on the log-likelihood in step2. Step 1 is then repeated using the information from Step 3. Slopes canreflect item discriminations and intercepts can reflect itemdifficulties.

In addition to the item slopes and intercepts, maximum a posteriori(MAP) latent trait scores, which reflect the patient's level of ability,may be computed for each patient.

The principal coding for IRT focuses on translating the mathematicalstructure chosen after CFA into one that can be assessed using IRT. Thedata used for the analysis may be, for instance, simply the patients'ratings on all items on which they were assessed. For consistency, themost recent available data for each patient on each item they wereadministered may be used. This has the convenience of putting patientscores in a particular frame of reference: typical discharge level. MAP(maximum a posteriori) scoring may be used, but other scoring methodsare known which could be employed instead, such as ML (maximumlikelihood), EAP (expected a posteriori), or MI (multiple imputation).Additionally there are different estimation methods that could beemployed. For instance, marginal maximum likelihood using theexpectation-maximization algorithm (MML-EM) may be used. However, thismethod can suffer when working with more than a few dimensions. In apreferred embodiment, the Metropolis-Hastings Robbins-Munro (MH-RM)estimation is used.

Maximum a posteriori (MAP) scoring requires two inputs: the scoringdensity of the population (usually assumed to be standard normal foreach dimension) and the IRT parameters for each item that a patient wasrated on. Multiplying the population density by the IRT functions foreach item results in what is known as a likelihood—in other words,mathematical representation of the probability of various scores, givenwhat is known about the items and how the patient was rated on each ofthe items. The location of the maximum value of that function is thepatient's MAP score.

Sometimes, response options on an item are only selected very rarely,which may cause problems with estimating IRT parameters for that item(and also implies that that response option may have been unnecessary).In such cases, those responses can be collapsed into an adjacentcategory. For example, if an item has responses {1, 2, 3, 4} andresponse 2 is very rarely seen in the data, we may recode the data {1,2, 2, 3}. It should be understood that the actual value of the number isunimportant in IRT analysis, and that instead, the ordinality matters.

Group composition: The IRT analyses used here can be multigroup innature to allow for more targeted assessment. For Self Care andMobility, patients may be grouped according to their level of balance(none, sitting, standing, and walking). Similarly, groups may be formedin the cognitive domain according to their cognitive diagnosis (stroke,brain injury, neurological, or none). This method can result in multipletest forms that only contain items appropriate for each patient. Forinstance, they may contain test forms as follows: for “Self-Care” and“Mobility”, no balance, sitting balance, (up to) standing balance, andno balance restrictions; for “Cognition,” stroke, brain injury,neurological, or not disordered. The forms may tailored according togroup membership, rather than to assessment areas. For example, thepatient's balance level may affect which balance measure items appear onthe Self Care and Mobility domains, while the patient's cognitivediagnosis (if any) may affect which measures may appear on the form. Forinstance, the ABS is only used on the Brain Injury form of the Cognitionmeasure and the KFNAP is only used on the Stroke measure.)

Item response theory results in a distinct score for each domain. Forexample, a patient may score a 1.2 in the “Self-Care” domain, a 1.4 inthe “Mobility” domain, and a 3 in the “Cognition” domain. In anembodiment, these scores may be reported to clinicians, patients, andothers separately. In other embodiments, these scores may be combinedinto a single score. In an embodiment, a score of +1 means the patientis 1 logit above average. A score of −1 means the patient is 1 logitbelow average. Values below −3 and above 3 are highly improbable,because the mathematical assumptions underlying IRT are such that scoresfollow an average distribution. It should be recognized by one orordinary skill in the art that other numbers reflecting the standarddeviation and logit could be employed instead. For instance, a score of3 could mean the patient is average, so scores would range between 0 and6. As another example, a score of 50 could mean the patient is average,and a score of +10 could mean that the patient is 1 logit above average,so scores would range from 20 to 80.

An example of running the IRT step 170 is now provided, with respect tothe Self-Care domain. Seven factors are provided to the IRT step 170:the Self Care factor, the Balance factor, the UE Functioning factor, theSwallowing factor, a hidden factor for ARAT, a hidden factor forovercoming a negative correlation between the FIST and FGA outcomesmeasures, and a hidden factor specific to the FIST so it is notoverweighted in the result. The IRT step 170 (for instance, using theMH-RM estimation) returns a discrimination matrix 172 and a difficultymatrix 174. For instance, these matrices may be presented inslope/intercept formulation, where slope reflects item discriminationand intercept reflects item difficulty.

Table 4 displays an exemplary discrimination matrix 172 for theSelf-Care domain of an exemplary IRT outcomes measure 180. The columnheadings a1-a7 in Table 4 represent the following, with “hidden” factorslisted in parentheses: (a1: Self Care; a2: (ARAT local dependence); a3:Upper Extremity Function; a4: Swallowing; a5: Balance; a6: (Reduction ofFIST influence); a7: (Negative relationship of BBS and FGA)). Table 4lists the slope values for each item for each factor a1-a7. The itemnaming in Table 4 is also reflected in Table 6 in Appendix 1, listingthe items in an exemplary IRT outcomes measure 180.

TABLE 4 Item a1 (a2) a3 a4 a5 (a6) (a7) 6 anteriorNudge 2.36 0 0 0 2.715.86 0 7 staticSitting 2.35 0 0 0 2.8 6.07 0 8 sittingEyesClosed 2.15 00 0 2.55 5.37 0 9 sittingLiftFoot 1.99 0 0 0 2.23 4.86 0 10 lateralReach2.25 0 0 0 2.5 5.45 0 11 pickUpFromFloor 1.78 0 0 0 1.92 4.3 0 1standingToSitting 0.7 0 0 0 4.05 0 2.44 2 stUnFtTgthr 0.89 0 0 0 5.65 03.4 3 rFwdArm 0.75 0 0 0 4.38 0 2.93 4 pickObjSt 0.72 0 0 0 4.24 0 2.795 turn360 0.92 0 0 0 5.25 0 3.48 12 levelSurface 1.01 0 0 0 1.81 0 −2.0813 verticalHeadTurns 0.95 0 0 0 1.86 0 −1.93 14 pivotTurn 1.17 0 0 02.15 0 −2.33 15 stepOverObstacle 1.03 0 0 0 2.04 0 −2.19 16ambulatingBackwards 1.05 0 0 0 2.02 0 −2.17 17 graspWood2p5 0.77 4.624.57 0 0 0 0 18 gripPourWater 0.87 5.11 5.61 0 0 0 0 19 pinchBearing3rd0.67 3.38 4.6 0 0 0 0 20 grossHandToMouth 0.48 3.24 3.21 0 0 0 0nhpLpoly 0 0 2.32 0 0 0 0 nhpRpoly 0 0 2.21 0 0 0 0 bbLpoly 0 0 3.63 0 00 0 bbRpoly 0 0 3.4 0 0 0 0 24 salivaC 1.84 0 0 2.99 0 0 0 25tongueMovementC 3.42 0 0 5.97 0 0 0 26 tongueStrengthC 3.26 0 0 5.6 0 00 27 tongueCoordinationC 2.79 0 0 4.8 0 0 0 28 oralPreparationC 2.31 0 04.04 0 0 0 29 bolusClearanceC 2.57 0 0 4.27 0 0 0 30 oralTransitC 2.46 00 4.22 0 0 0 31 voluntaryCoughC 1.59 0 0 2.74 0 0 0 32 pharyngealPhaseC2.11 0 0 3.67 0 0 0 33 pharyngealResponseC 1.29 0 0 2.2 0 0 0 foisC 1 00 1.62 0 0 0 ricdssC 1.32 0 0 1.88 0 0 0 eatingC 2.37 0 0 0 0 0 0groomingC 3.49 0 0 0 0 0 0 bathingC 3.9 0 0 0 0 0 0 dressingUpperC 4.870 0 0 0 0 0 dressingLowerC 5.63 0 0 0 0 0 0 toiletingC 5.08 0 0 0 0 0 0

Table 5 displays an exemplary difficulty matrix 174 for an IRT outcomesmeasure 180. Table 5 displays the intercept values for each item, foreach factor d1-d6. The column headings d1-d6 in Table 5 represent thefollowing, with “hidden” factors listed in parentheses: (d1: Self Care;d2: (ARAT local dependence); d3: Upper Extremity Function; d4:Swallowing; d5: Balance; d6: (Reduction of FIST influence)).

TABLE 5 Item d1 (d2) d3 d4 d5 (d6) anteriorNudge 9.88 8.52 6.16 5.29staticSitting 13.1 9.96 7.88 6.48 sittingEyesClosed 9.4 7.99 6.39 5.08sittingLiftFoot 7.12 5.88 4.37 3.28 lateralReach 7.92 5.35 3.48 2.15pickUpFromFloor 4.82 3.74 2.69 1.71 standingToSitting 2.07 0.65 −0.24−3.13 stUnFtTgthr 0.04 −1.88 −2.36 −6 rFwdArm 0.19 −2.63 −3.3 −5.7pickObjSt −1.36 −1.87 −2.01 −5.63 tum360 −2 −4.96 −7.25 −7.79levelSurface 4.18 0.41 −2.28 verticalHeadTurns 3.64 1.1 −2.5 pivotTurn3.92 0.83 −1.89 stepOverObstacle 3.22 −0.87 −2.93 ambulatingBackwards3.07 0.29 −3.45 graspWood2p5 5.25 3.4 −0.19 gripPourWater 4.22 2.54 −2.2pinchBearing3rd 0.66 −2.66 grossHandToMouth 5.68 4.1 1.24 nhpLpoly 1.570.14 −1.17 nhpRpoly 1.75 0.44 −0.81 bbLpoly 1.74 0.08 −1.92 bbRpoly 1.950.1 −1.72 salivaC 3.78 2.76 1.01 0.35 tongueMovementC 5.26 2.22 −0.26−3.66 tongueStrengthC 0.39 −1.37 −4.94 tongueCoordinationC 3.11 −0.08−3.2 oralPreparationC 1.23 0.76 0.11 −2.09 bolusClearanceC 1.42 −0.5−4.08 oralTransitC 2.95 1.53 −0.44 −3.33 voluntaryCoughC 1.15 −0.11−1.56 pharyngealPhaseC 3.66 1.45 −3.12 pharyngealResponceC 2.3 −0.14foisC 0.8 0.41 0.18 −0.02 −0.79 −1.05 ricdssC 1.68 0.58 −0.29 −1 −1.65eatingC 2.37 1.9 1.55 −1.32 −3.26 groomingC 3.31 1.77 0.74 −3.09 −8.07bathingC 1.72 0.63 −0.74 −2.35 −6.73 dressingUpperC 3.62 2.24 0.94 −0.62−4.97 −11.37 dressingLowerC 1.67 −0.15 −1.58 −3.7 −7.37 −14.8 toiletingC0.72 −0.48 −1.47 −2.92 −6.25It should be understood that discrimination matrix 172 and a difficultymatrix 174 may be prepare for each domain in the IRT outcomes measure180.

An exemplary score/probability response may be plotted, where the X-axisreflects the score and the Y-axis reflects the probability of response.The product of the curves results in a likelihood curve that somewhatappears like a bell curve. The peak of the curve can be used as thescore for the patient.

Input from Therapists to Ensure Clinical Relevance.

Each item may be labeled with a cluster that most appropriatelydescribes its role in the IRT outcomes measure 180. This labeling may bedone by clinicians on the basis of their education, training, andexperience. For example, a clinician may label an item that measuresbalance, such as items that test function in sitting, as falling withinthe “Mobility” domain and the “Balance” factor in Table 1.

Because item selection (retention or removal) in the item reduction stepof the analysis is predicated on psychometric and statisticalevaluations, in an embodiment, clinical experts may review the itemcontent covered in the reduced item sets for further feedback. Forexample, a pool of clinicians may be surveyed for input on whether itemsshould be added or removed from the subsets taken from each of the fulloutcomes measures. Their input may be used to construct the final modelsfor each domain, to help ensure the retained items are psychometricallysound and clinically relevant.

Remodeling to Derive Final Sets of Items.

After the negotiated item sets, considering both psychometric evaluationand clinical judgement, were in place, the CFA and IRT steps may becarried out. Left-out items with a large clinical endorsement may beadded back into the models, while included items with low endorsementmay be removed. The fit of the models to the data may then be assessedusing the root mean square error of approximation (RMSEA) computedduring the CFA, and new item parameter estimates and latent trait scoreswere computed during the IRT analysis. Table 6 in Appendix 1 to thisSpecification lists the items in a preferred exemplary IRT outcomesmeasure 180.

Display

Various aspects of data relating to an individual patient's score may bedisplayed for a clinician and/or a patient.

FIG. 3 displays an exemplary scoring system of the IRT, comparing itagainst a FIM® score known in the prior art. The IRT score reflects theamount of ability that a person, such as a patient, has. The IRT scorecan be a continuously scaled score across all functional categories. Ascore of exactly 0 means that the person has average ability atdischare. A score above 0 means that the person has above averageability. A score below 0 means that the person has below averageability. FIG. 3 displays the FIM scoring for a dressing item on the FIMand the continuum of attainable Self Care IRT scores. The FIM scores arereflected by the length of each patterned section. For example, thepatterned section labeled “1” reflects a FIM score of 1; the sectionlabeled “2” reflects a FIM score of 2; and so on. Scores anddifficulties are presented on the same metric, meaning that if someonehas an IRT score of 1.50, they would be expected to score in the 6thcategory of the item.

The value of the IRT score becomes apparent from an analysis of FIG. 3.Suppose a patient is admitted to an inpatient rehabilitation facility,and their IRT score improves from −1.00 to 0.00. The equivalent changein the FIM level would be a +3. As a result, this would be seen as agood outcome for the patient, as the patient showed functional gains.

However, the FIM score is deficient in showing gains when progress ismade within a FIM level. Suppose another patient is admitted with ascore of −2.00, and progresses all the way to −1.00. Even though thepatient made just as much progress as the previous patient (+1.00), itstill looks like the patient has not improved her functional level onupper body dressing, as the change in the FIM for this item is 0. As aresult, one benefit of the IRT score is that it can detect improvementwhere the FIM cannot. In our experience, the expected change in SelfCare for individuals with Nontraumatic spinal cord injury andNeurological injuries is fairly dramatic when using the IRT.

FIG. 4 displays an exemplary plot of certain data relating to patientscores in the Self Care, Cognition, and Mobility domains. The percentagevalues 25%, 50%, 75%, and 100% reflect the percent of a score on eachdomain. For instance a score of 100% on the Self Care domain reflects apatient who received the highest possible score on that domain. Thesolid line triangle reflects the patient's initial scores, which may betabulated based on assessments at or shortly after admission. The blacktriangle reflects the patient's current scores. The dashed-linestriangle reflects the patient's predicted scores. By reviewing thescores in this way, a clinician is able to easily determine the domainsin which a patient has made improvement and also easily determine thedomains where additional therapy or other care may be useful. Forinstance, after reviewing the plot at FIG. 4, a clinician may determinethat further care should be focused in the area of Self Care andMobility, since those scores are below the predicted scores for thosedomains.

Prediction estimates may be derived in a variety of ways. In oneembodiment, Hierarchical Linear Modeling (HLM) may be used,incorporating information regarding past patients' diagnoses, theseverity of those diagnoses (the “case mix group”, a measure of thepatient's condition's severity within a diagnosis), the days on whichmeasures were administered to the patients, and the scores on thosedays. The modeling may output a predictive curve for every severitywithin every diagnosis for up to 50 days of inpatient stay. Whenplotting the information, the x-axis may be the number of days sinceadmission and the y-axis may be the IRT (MAP) score.

Other methods of prediction could be used, including data sciencemethods like neural networks and random forest models. Furthermore,additional patient information may be incorporated in the predictionprocess.

In an embodiment, a patient may be assessed using the IRT outcomesmeasure 180 over multiple days. For instance, the patient may beassessed on a first subset of questions from the IRT outcomes measure180 on a first day, and then assessed on a second subset of questions ona second day. The data feed may be set up such that it collects the mostrecent item value.

Adaptive testing may be employed, such that the items in the IRToutcomes measure 180 are selected for assessment in response to thescore from an already assessed item. For example, the clinician mayassess the patient with the items in the IRT outcomes measure 180 fromthe FIST test; compute an initial IRT score based off the results; andthen select a next item (or a plurality of next items) most appropriate,based on the initial IRT score. This process may be applied iterativelyuntil the patient's score can be determined to be accurate within apre-determined uncertainty level. For instance, once uncertainty is ator below 0.3, the adaptive testing method may stop providing additionalitems for assessment and provide a final IRT score for the patient,clinician, or others to review.

FIG. 5 displays an exemplary chart of certain data relating to patientscores in the Self Care domain. Each row of the chart relates to asingle item. For example, the top row relates to the test of grasping awood block. Each row of the chart is divided into different shadings, asis discussed with respect to FIG. 3. The length of each section reflectshow a score on that item relates to the AQ score. For example, sectionb1 reflects how a score of 1 on a grasping item relates to the AQ score.

FIG. 5 further shows a patient's current and expected functional statuson each of the items/tasks in the Self Care domain. It should beunderstood that this chart could display data from Mobility, Cognition,or other domains. The “Choose Length of Stay” scroll-bar allows aclinician to compare each level of ability on every item (e.g., currentvs. expected) for various lengths of stay. This can allow a clinician todetermine whether additional days of inpatient stay would likely benefita patient, and if so, by how much.

A clinician may review the IRT score with the patient's score on aparticular FIM item to determine whether additional interventions areappropriate. For example, if the patient has AQ score of 1, a score of 4on the FIM toileting measure is expected. But, if the FIM toiletingmeasure is lower, the clinician can use that as an indication to adjusttherapy to specifically target improved toileting.

FIG. 6A and FIG. 6B are a “FIM Explorer” section, with features to allowclinicians to select and/or set the goal for each FIM-specific task. Forinstance, ‘4—Minimal Assistance’ is chosen in FIG. 6A as a treatmentgoal for eating task. Once a task-specific goal is chosen, a comparisonchart shown at FIG. 7 may be displayed. This chart can allow a therapistor other clinician to compare whether the goals are set too high or toolow when compared with the vertical line on the chart. The vertical lineis derived from the selected goal ratings and converted into an IRTscore.

FIG. 8 displays various plots for the Self Care domain in comparisonwith a patient's FIM score. As shown in FIG. 8, the assessment areaswithin the Self Care domain include Balance, Upper Extremity Function,and Swallowing. In one embodiment, incomplete FIM administration may beomitted from the plot, to avoid confusion about whether the score is lowor merely incomplete.

Prediction

Prediction of AQ score may be based on various factors, such as medicalservice group; case mix group (CMG); and/or lengths of stay. Within CMG,age may be a factor used to assist in the prediction.

Data generated by predictive models may be used in various ways. Forexample, a patient's length of stay can be predicted via his/her medicalcondition, level of impairment, and other demographic and clinicalcharacteristics. As another example, if a patient is below theirprediction on a given domain, clinicians can target those areas for morefocused therapies. As another example, if a patient's progress in onedomain has begun to taper off, clinicians could note this and prioritizebalanced treatment in that domain. As another example, given somefinancial information, it would be possible to assess the dollar valueof expected improvement over a period of time and compare it to the costof inpatient care over that same time frame. Discharge decisions couldbe made using the ratio of value of care to cost of care. Additionally,predicting success in other treatment settings is possible. Givensimilar assessments in other levels and locations of care (e.g.,outpatient, SNF, etc.), a prospective look at the course of improvementin those settings could be determined. Better decisions regarding carein those settings could potentially be made.

APPENDIX 1 No. Item Instructions Responses Domain and Area/Cluster 1Standing to Please sit down. 4) Sits safely with minimal use of handsDomain: Self Care Sitting 3) Controls descent by using handsArea/Cluster: Balance 2) Uses back of legs against chair to controldescent Domain: Mobility 1) Sits independently but has uncontrolleddescent Area/Cluster: Balance 0) Needs assistance to sit 2 StandingPlace your feet together and stand 4) Able to place feet togetherindependently and stand for Domain: Self Care Unsupported withoutholding. 1 minute safely Area/Cluster: Balance with Feet 3) Able toplace feet together independently and stand for Domain: MobilityTogether 1 min with supervision Area/Cluster: Balance 2) Able to placefeet together independently and to hold for 30 seconds 1) Needs help toattain position but able to stand 15 seconds feet together 3 ReachingLift arm to 90 degrees. Stretch out 4) can reach forward confidently >25 cm (10 inches) Domain: Self Care Forward with your fingers and reachforward as far 3) can reach forward > 12.5 cm safely (5 inches)Area/Cluster: Balance Outstretched as you can. (Examiner places a ruler2) can reach forward > 5 cm safely (2 inches) Domain: Mobility Arm whileat end of fingertips when arm is at 90 1) reaches forward but needssupervision Area/Cluster: Balance Standing degrees. Fingers should nottouch the 0) loses balance while trying/requires external support rulerwhile reaching forward. The recorded measure is the distance forwardthat the finger can reach while the subject is in the most forward leanposition. When possible, ask subject to use both arms when reaching toavoid rotation of the trunk.) 4 Pick Up Pick up the shoe/slipper whichis 4) able to pick up object safely and easily Domain: Self Care ObjectFrom placed in front of your feet. 3) able to pick up object but needssupervision Area/Cluster: Balance the Floor May use an empty tissue boxfor 2) unable to pick up object but reaches 1-2 in. from object, Domain:Mobility From a testing instead of the shoe/slipper. keeps balanceindependently Area/Cluster: Balance Standing 1) unable to pick up andneeds supervision while trying Position 0) unable to try/needs assist tokeep from losing balance or falling 5 Turn 360 Turn completely around ina full 4) able to turn 360 degrees safely in 4 seconds or less Domain:Self Care Degrees circle. Pause. Then turn a full circle 3) able to turn360 degrees safely one side only in 4 Area/Cluster: Balance in the otherdirection. seconds or less Domain: Mobility 2) able to turn 360 degreessafely but slowly Area/Cluster: Balance 1) needs close supervision orverbal cueing 0) needs assistance while turning 6 Anterior Lightanterior nudge to superior 4) Independent (completes task independently& Domain: Self Care Nudge sternum successfully) Area/Cluster: Balance 3)Verbal cues/increased time (completes task Domain: Mobilityindependently & successfully and only needs more Area/Cluster: Balance 7Static Have patient sit for 30 seconds time/cues) Domain: Self CareSitting 2) Upper extremity support (must use UE for support orArea/Cluster: Balance assistance to complete successfully) Domain:Mobility 1) Needs assistance (unable to complete without physicalArea/Cluster: Balance 8 Sitting, Eyes Have patient sit with eyes closedfor assist) Domain: Self Care Closed 30 seconds 0) Dependent (requirescomplete physical assist, unable to Area/Cluster: Balance completesuccessfully even with physical assist) Domain: Mobility Area/Cluster:Balance 9 Sitting, Left Have patient sit, dominant side, lift Domain:Self Care Foot foot 1 inch twice Area/Cluster: Balance Domain: MobilityArea/Cluster: Balance 10 Lateral Reach Have patient use dominant arm,clear Domain: Self Care opposite ischial tuperosity Area/Cluster:Balance Domain: Mobility Area/Cluster: Balance 11 Pick Up Have patientpick up object from Domain: Self Care Object from floor, from betweenfeet Area/Cluster: Balance Floor Domain: Mobility Area/Cluster: Balance12 Gait, Level Walk at your normal speed from here 3) Normal: Walks 6 m(20 ft) in less than 5.5 seconds, no Domain: Self Care Surface to thenext mark (6 m [20 ft]). assistive devices, good speed, no evidence forimbalance, Area/Cluster: Balance normal gait pattern, deviates no morethan 15.24 cm (6 in) Domain: Mobility outside of the 30.48-cm (12-in)walkway width. Area/Cluster: Balance 2) Mild impairment: Walks 6 m (20ft) in less than 7 seconds but greater than 5.5 seconds, uses assistivedevice, slower speed, mild gait deviations, or deviates 15.24-25.4 cm(6-10 in) outside of the 30.48-cm (12-in) walkway width. 1) Moderateimpairment-Walks 6 m (20 ft), slow speed, abnormal gait pattern,evidence for imbalance, or deviates 25.4-38.1 cm (10-15 in) outside ofthe 30.48-cm (12-in) walkway width. Requires more than 7 seconds toambulate 6 m (20 ft). 0) Severe impairment-Cannot walk 6 m (20 ft)without assistance, severe gait deviations or imbalance, deviatesgreater than 38.1 cm (15 in) outside of the 30.48-cm (12-in) walkwaywidth or reaches and touches the wall. 13 Gait with Walk from here tothe next mark (6 m 3) Normal-Performs head turns with no change in gait.Domain: Self Care Vertical [20 ft]). Begin walking at your Deviates nomore than 15.24 cm (6 in) outside 30.48-cm Area/Cluster: Balance HeadTurns normal pace. Keep walking straight; (12-in) walkway width. Domain:Mobility after 3 steps, tip your head up and 2) Mild impairment-Performstask with slight change in Area/Cluster: Balance keep walking straightwhile looking gait velocity (eg, minor disruption to smooth gait path),up. After 3 more steps, tip your head deviates 15.24-25.4 cm (6-10 in)outside 30.48-cm down, keep walking straight while (12-in) walkway widthor uses assistive device. looking down. Continue alternating 1) Moderateimpairment-Performs task with moderate looking up and down every 3 stepschange in gait velocity, slows down, deviates 25.4-38.1 until you havecompleted 2 cm (10-15 in) outside 30.48-cm (12-in) walkway widthrepetitions in each direction. but recovers, can continue to walk. 0)Severe impairment-Performs task with severe disruption of gait (eg,staggers 38.1 cm [15 in] outside 30.48-cm (12-in) walkway width, losesbalance, stops, reaches for wall). 14 Gait and Instructions: Begin withwalking at 3) Normal-Pivot turns safely within 3 seconds and stopsDomain: Self Care Pivot Turn your normal pace. When I tell quickly withno loss of balance. Area/Cluster: Balance you,“turn and stop,” turn asquickly 2) Mild impairment-Pivot turns safely in >3 seconds and Domain:Mobility as you can to face the opposite stops with no loss of balance,or pivot turns safely within Area/Cluster: Balance direction and stop. 3seconds and stops with mild imbalance, requires small steps to catchbalance. 1) Moderate impairment-Turns slowly, requires verbal cueing, orrequires several small steps to catch balance following turn and stop.0) Severe impairment-Cannot turn safely, requires assistance to turn andstop. 15 Step Over Begin with walking at your normal 3) Normal-Is ableto step over 2 stacked shoe boxes Domain: Self Care Obstacle pace. WhenI tell you,“turn and stop,” taped together (22.86 cm [9 in] totalheight) without Area/Cluster: Balance turn as quickly as you can to facethe changing gait speed; no evidence of imbalance. Domain: Mobilityopposite direction and stop. 2) Mild impairment-Is able to step over oneshoe box Area/Cluster: Balance (11.43 cm [4.5 in] total height) withoutchanging gait speed; no evidence of imbalance. 1) Moderate impairment-Isable to step over one shoe box (11.43 cm [4.5 in] total height) but mustslow down and adjust steps to clear box safely. May require verbalcueing. 0) Severe impairment-Cannot perform without assistance. 16Ambulating Walk backwards until I tell you to 3) Normal-Walks 6 m (20ft), no assistive devices, good Domain: Self Care Backwards stop. speed,no evidence for imbalance, normal gait pattern, Area/Cluster: Balancedeviates no more than 15.24 cm (6 in) outside 30.48-cm Domain: Mobility(12-in) walkway width. Area/Cluster: Balance 2) Mild impairment-Walks 6m (20 ft), uses assistive device, slower speed, mild gait deviations,deviates 15.24-25.4 cm (6-10 in) outside 30.48-cm (12-in) walkwaywidth. 1) Moderate impairment-Walks 6 m (20 ft), slow speed, abnormalgait pattern, evidence for imbalance, deviates 25.4-38.1 cm (10-15 in)outside 30.48-cm (12-in) walkway width. 0) Severe impairment-Cannot walk6 m (20 ft) without assistance, severe gait deviations or imbalance,deviates greater than 38.1 cm (15 in) outside 30.48-cm (12-in) walkwaywidth or will not attempt task. 17 Grasp Pickup 2.5 cm block 3 = task iscompleted in less than 5 seconds; appropriate Domain: Self Care bodyposture; normal hand movement components; Area/Cluster: Upper normal armmovement components Extremity Function 18 Grip Pour water from one glassto another 2 = task is completed but either with great difficulty orDomain: Self Care takes abnormally long. “Great difficulty” meansabnormal Area/Cluster: Upper hand movement components (i.e. wronggrasp), abnormal Extremity Function 19 Pinch Grasp the ball bearing (ormarble) arm movements (i.e. elbow does not flex as required), or Domain:Self Care using these fingers, lift it up, and abnormal body movements(i.e. trunk compensations), Area/Cluster: Upper place it in the tin ontop of the shelf. “abnormally long” means between 5-60 seconds.Extremity Function Patient attempts to lift the (6 mm) 1 = Patient onlypartially completes the task within the 60 ball bearing (or marble) with3^(rd) seconds, regardless of hand/arm movements patterns or finger andthumb postural requirements. For grasp, grip and pinch 20 Hand to Touchyour mouth with the palm of subscales, score is not attainable withoutsome form of Domain: Self Care mouth your hand. hand movement. Simplypushing the object across table Area/Cluster: Upper does not = 1.Extremity Function 0 = Given when the patient is unable to complete anypart of the hand or arm movement within 60 seconds. 21 Dexterity I wantto see how quickly you can Domain: Self Care pick up one block at a timewith your Area/Cluster: Upper right (or left) hand. Carry it to theExtremity Function other side of the box and drop it. Make sure yourfingertips cross the partition. 22 Peg Test “Pick up the pegs one at atime, using Domain: Self Care one hand only, and put them in theArea/Cluster: Upper holes as quickly as possible. You can ExtremityFunction do them in any order until all of the holes are filled. Then,without pausing, remove the pegs one at a time and return them to thecontainer as quickly as you can. We will do this two times with eachhand.” 23 Functional Domain: Self Care Oral Intake Area/Cluster: ScaleSwallowing 24 Saliva Observe the patient's control of 5) NAD Domain:Self Care saliva. Note any escape of secretions 4) frothy/expectoratedArea/Cluster: from the side of the mouth, and check 3) drooling at timesSwallowing comers of mouth for wetness. Ask 2) some drool consistentlythe patient if he or she has noticed 1) gross drool undue saliva lossduring the day, at night, or while side lying. 25 Tongue AnteriorAspect: 10) full range of motion (“ROM”) Domain: Self Care MovementProtrusion - have patient extend 8) mild impairment in rangeArea/Cluster: tongue as far forward as possible and 6) incompletemovement Swallowing then retract similarly. 4) minimal movementLateralization - have patient touch 2) no movement each corner of themouth, then repeat alternating lateral movements. With tongue, havepatient attempt to clear out lateral sulci on each side of the mouth.Elevation - With mouth open wide, have patient raise tongue tip toalveolar ridge. Alternate elevation and depression in this way.Posterior Aspect: Elevation - have patient raise back of tongue to meetpalate and hold the position 26 Tongue Have patient push laterally,against a 10) NAD Domain: Self Care Strength tongue depressor or glovedfinger. 8) minimal weakness Area/Cluster: Have patient push anteriorly,against 5) unilateral weakness Swallowing a tongue depressor or glovedfinger. 2) gross weakness Have patient push during elevation anddepression of the tongue. Ask patient to elevate back of tongue againsta tongue depressor or gloved finger. Note tone and strength toresistance. 27 Tongue Ask patient to lick around lips, 10) NAD Domain:Self Care Coordination slowly and then rapidly, touching all 8) mildincoordination Area/Cluster: parts. 5) gross incoordination SwallowingHave patient rapidly repeat tongue 2) no movement unable to assess ripealveolar syllables /ta/. Repeat a sentence including tongue tip alveolarconsonants e.g. Take Tim to tea). Ask patient to rapidly repeat velarsyllables /ka/. Repeat a sentence including velar consonants e.g., Canyou keep Katie clean?). 28 Oral Observe patient while eating or 10) NADDomain: Self Care Preparation chewing. Ask to observe how bolus is 8)lip or tongue seal bolus escape Area/Cluster: prepared prior toswallowing. Check 6) minimal chew thrust gravity assisted Swallowing forloss from mouth, position of food 4) no bolus formation no attemptbolus, spread throughout oral cavity, 2) unable to examine and loss ofmaterial into lateral or anterior sulci. Note chewing movements andfatigue. 29 Bolus Observe patient eating/swallowing a 10) fully clearedDomain: Self Care Clearance bolus. 8) significant clearance/minimalresidue Area/Cluster: Check oral cavity for residue 5) someclearance/residue Swallowing following a swallow. 2) no clearance 30Oral The clinician will position a hand 10) NAD, triggers rapidly within1 second Domain: Self Care Transit under the patient's chin, withfingers 8) delay > 1 sec Area/Cluster: spread as per manual palpation 6)delay > 5 sec Swallowing method Logemann, 1983). Use only a 4) delay > 4sec light touch. Ask the patient to 2) no movement observed swallow.Compare time elapsed between the initiation of lingual movement untilthe initiation of hyoid and laryngeal rise. 31 Voluntary Ask the patientto cough as strongly 10) NAD, strong clear cough Domain: Self Care Coughas possible. Observe strength and 8) attempt bovine Area/Cluster:clarity of cough. 5) attempt inadequate Swallowing 2) no attempt/unableto assess 32 Pharyngeal Observe hyoid and laryngeal 10) immediatelaryngeal elevation clearance of material Domain: Self Care Phasemovement using manual palpation 8) laryngeal elevation mildly restrictedslow initiation Area/Cluster: method Logemann, 1983). Note incompleteclearance Swallowing smoothness of excursion and 5) pooling/gurglinglaryngeal elevation incomplete maximal elevation point. Following 2) noswallow unable to assess swallow, ask patient to phonate/ah/ for severalseconds. Note vocal quality. Ask patient to pant following swallow thenvocalize. Note vocal quality Ask patient to turn head to each side andvocalize. Note vocal quality. Ask patient to lift chin and vocalize.Note vocal quality. 33 Pharyngeal Observe vocal quality and coughing 10)NAD Domain: Self Care Response as a result of swallow. To be 5) coughbefore/during/after swallow Area/Cluster: completed in association withother 1) not coping/gurgling Swallowing assessment tasks. 34 RICDysphagia supervision instructions. 6) No cueing needed to safelytolerate diet. Independent. Domain: Self Care Dysphagia 5) Needs cues10% of the time to safely tolerate diet. Area/Cluster: SupervisionStandby prompting. Swallowing Scale 4) Needs cues 10-25% of the time tosafely tolerate diet. Minimal cues. 3) Needs cues 25-50% of the time tosafely tolerate diet. Moderate cues. 2) Needs cues 50-75% of the time tosafely tolerate diet. Maximal cues. 1) Needs cues 75-100% of the time tosafely tolerate diet. Total assistance. 0) For NPO patients and patientswho are physically dependent for eating. 35 Pressure Pressure reliefinstructions Domain: Mobility Relief Area/Cluster: Wheelchair Skills 365 Time Sit Patient sits with arms folded across Record amount of timerequired to complete the test. Domain: Mobility to Stand chest and withtheir back against a Timing begins at “Go” and stops when the patient'sArea/Cluster: Changing chair. For patients with history of buttockstouch the chair on the fifth repetition. Body Positions stroke, it isacceptable to have the Domain: Mobility impaired arm at their side or ina Area/Cluster: Mobility sling. Use a chair at height from 43-45 cm.Ensure that the chair is not secured (i.e against the wall or mat).Instructions: “I want you to stand up and sit down 5 times as quickly asyou can when I say ‘Go’.” Instruct to stand fully between repetitions ofthe test and not to touch the back of the chair during each repetition.37 Bed Mobility Domain: Mobility (Prone) Area/Cluster: Changing BodyPositions Domain: Mobility Area/Cluster: Bed Mobility 38 Short Sit toDomain: Mobility Bed/Mat Area/Cluster: Bed (Supine) Mobility 39 Supineto Domain: Mobility long sit/ring Area/Cluster: Bed sit Mobility 40 SelfROM Domain: Mobility Area/Cluster: Bed Mobility 41 Timed Stair Domain:Mobility Climb Area/Cluster: Mobility 42 6 Minute Domain: Mobility WalkArea/Cluster: Mobility 43 Borgs RPE Domain: Mobility Area/Cluster:Mobility 44 10 Meter Domain: Mobility Walk Test Area/Cluster: Mobility45 6 Minute Domain: Mobility Push Area/Cluster: Mobility 46 City Patientshould name the city they are 3: Correct Spontaneous or upon first freerecall attempt Domain: Cognition currently in 2: Correct upon logicalcueing (i.e. that was yesterday, so Area/Cluster: Cognition 47 Name ofPatient should name the hospital they today . . .) Domain: CognitionHospital are currently at 1: Correct upon multiple choice or phonemiccueing Area/Cluster: Cognition 48 Month Patient should know the current0: incorrect despite cueing, inappropriate response or Domain: Cognitionmonth unable to respond. Area/Cluster: Cognition 49 Year Patient shouldknow the current year Domain: Cognition Area/Cluster: Cognition 50 ClockTime Patient should know the time, may Domain: Cognition be +/−30minutes. They can look at a Area/Cluster: Cognition clock withoutpenalty 51 Etiology/Event Patient should know what brought Domain:Cognition them into the hospital (i.e. what Area/Cluster: Cognitionbrought you in today?) 52 Gaze No instructions to the patient. 3: Thepatient is easily able to direct his gaze toward the Domain: CognitionOrientation Instructions to clinician: observe right side of the spacebut does not attempt to orient the Area/Cluster: Cognition patient gaze.eyes toward the left side. 2: There are constant and clear asymmetriesin the gaze direction toward the left and right sides of space. Thepatient explores the environment by looking toward the right first, andafter a long delay, slowly looks toward the left. During the entiresession, the patient spends much more time looking to his right side. 1:There are inconsistent but observable asymmetries in the gaze directiontoward the left and right sides of space. The patient explores anenvironment by looking toward the right first, and then slowly towardthe left with some hesitation. During the entire session the patientlooks toward the right more than the left. 0: The patient spontaneouslydirects his/her gaze toward the right and left sides of space withouthesitation and without any prompting. 53 Limb No instructions topatient. 3: The patient completely ignores the left limbs and neverDomain: Cognition Awareness Instructions to clinician: observe attempts,with the assistance of the right hand, to move the Area/Cluster:Cognition patient use of limbs. left arm or and leg, or verballyacknowledge any discomfort in the left arm and leg. You cannot observeany spontaneous caring for the left limbs. 2: Time spent caring for theleft limbs is short with incomplete performance. For example, during theentire session, they care for their left arm once, by moving it over tothe arm rest, but for the remainder of the session they do not care muchfor it and let it accidentally hang outside the chair. Another example,is when asked to wash their hands, they do not wash their left hand oronly wash it incidentally. Or you may think of the entire session in acontinuous time period. If the patient takes care of their left limbonly ⅓ of the time, give them a score of 2. 1: If the patient takes careof their left limbs ⅔ of the time, you give them a score of 1. 0: Thepatient pays attention and cares for their left limbs or as much as theydo for their right limbs. Even if they complain of difficulty moving theleft limbs or ask for help, because it means they pay attention to theleft limbs. 54 Auditory No instructions to patient. 3: The patient showsan immediate reaction to the sound Domain: Cognition AttentionInstructions to clinician: Make sure from the right side but no reactionfrom the left side at all. Area/Cluster: Cognition you are out of thepatient's sightlines, 2: The patient shows immediate reaction to thesound from and then without warning, make a the right side, but thereaction from the left side is loud noise to the patients right or leftinadequate or incorrect. For example, the patient may side. You can dropan object or clap state they heard something but is not able to identifythe loudly. Do it once on the left side, location or the noise. Theyshift their head or body to the and once to the right side later.opposite side from which the noise is actually coming. Observe whetherpatient has an 1: The patient immediately reacts to the sound from theimmediate reaction like startle, right correctly but takes an observablylonger time or blinking, or wincing. hesitates to the sound from theleft. 0: All the reactions observed are correct and immediate on bothleft and right sides. 55 Personal Ask patient for the location of 3 3:The patient always locates and points to objects on their Domain:Cognition Belongings personal belongings on the patient's right side byfails to locate any objects on the left side. Area/Cluster: Cognitionright and 3 on the patient's left. To 2: The patient always locates andpoints to objects on their be considered a personal belonging right sidebut fails to locate ⅔ of the objects on the left the item must almostalways be kept side. at a certain location by the patient. 1: Thepatient always locates and points to the objects on Do not hide orarrange the objects for their right side but fails to locate and pointto ⅓ of the the patient to find, preferred locations objects on the leftside. should be determined by the patient. 0: The patients does nothesitate to locate and point to In asking for the object location,objects on the right and left side. phrase the question “I can't find x,can you tell me where it is?”. Observe how the patient looks around tolocate the object and explore their environment 56 Dressing Ask patientto put on an open front 3: The patient only attempts to dress the rightarm and Domain: Cognition shirt or coat. Look for differences incompletely ignores the left, making no attempt to put the Area/Cluster:Cognition performance on the left and right left arm through the sleeve,and odes not acknowledge a sides of the body. need for help. 2: Thepatient does not acknowledge a need for help. They start by puttingtheir right arm in the sleeve and continue the left arm. However, theyspend significantly less time in dressing their left arm, and the shirtis very messy on the left side. In the end the performance on the leftis incomplete and ineffective. 1: The patient does not acknowledge theneed for help. They may first attend to their right side, putting theirright arm in the sleeve and eventually with some hesitation, work theleft arm into its sleeve as well. In the end, the patient is able to puton the shirt, but the left side is not completely pulled down or doesnot appear as nicely as the right side. The patient does not acknowledgea need for help. 0: The patient asks for help with the left side of thebody, and is paying attention to his/her left arm by trying hard tocomplete the task on the left side. Assessment measures awareness ofdisability and so a 0 may be given to a patient who cannot complete thetask but asks for help as it indicates they are not neglecting the leftarm. 57 Grooming Ask the patient to perform 3 3: In all three tasks, thepatient only pays attention to the Domain: Cognition grooming tasks.right and always ignores the left side. Area/Cluster: Cognition 2: Thepatient always takes care of the right side first, and miss the leftside in at least one of the tasks. 1: The patient completes all threetasks in a satisfactory manner. They always take care of the right sidefirst, and spend significantly shorter time and put in less effort onthe left side. 0: The patient completes all three tasks with no apparentleft side asymmetry. 58 Distraction Observe the patient for shortattention 1 = absent: the behavior is not present. Domain: Cognitionspan, easy distractibility, and 2 = present to a slight degree: thebehavior is present but Area/Cluster: Cognition inability toconcentrate. does not prevent 59 Impulsiveness Observe the patient forindications of the conduct of other, contextually appropriate behavior.Domain: Cognition impulsiveness, impatience, and low (The individualArea/Cluster: Cognition tolerance for pain or frustration. may redirectspontaneously, or the continuation of the 60 Cooperation Observe thepatient for uncooperative agitated behavior Domain: Cognition behavior,resistance to care, and does not disrupt appropriate behavior.)Area/Cluster: Cognition demanding behavior. 3 = present to a moderatedegree: the individual needs to 61 Pulling Observe the patient forpulling at be redirected from Domain: Cognition tubes, restraints, etc.an agitated to an appropriate behavior, but benefits from Area/Cluster:Cognition 62 Repetition Observe the patient for repetitive such cueing.Domain: Cognition behaviors, motor and/or verbal. 4 = present to anextreme degree: the individual is not able Area/Cluster: Cognition toengage in appropriate behavior due to the interference of the agitatedbehavior, even when external cueing or redirection is provided. 63Behavioral Domain: Cognition Observation Area/Cluster: Cognition Profile64 Pragmatic Domain: Cognition Communi- Area/Cluster: Cognition cationSkills 65 Delayed Domain: Cognition Recall 3 Area/Cluster: Memory Words66 Rivermead Domain: Cognition Immediate Area/Cluster: Memory StoryRetell 67 Rivermead Domain: Cognition Delayed Area/Cluster: Memory StoryRetell 68 Basic word Domain: Cognition description Area/Cluster:Communication 69 Commands Domain: Cognition Area/Cluster: Communication70 Complex Domain: Cognition Ideation Area/Cluster: Communication 71Word Domain: Cognition repetition Area/Cluster: Communication 72Sentence Domain: Cognition repetition Area/Cluster: Communication 73Form Domain: Cognition Area/Cluster: Communication 74 Letter Domain:Cognition choice Area/Cluster: Communication 75 Motor Domain: Cognitionfacility Area/Cluster: Communication 76 Picture-word Domain: Cognitionmatching Area/Cluster: Communication 77 Oral word Domain: Cognitionreading Area/Cluster: Communication 78 Oral sentence Domain: Cognitionreading Area/Cluster: Communication 79 Sentence/para- Domain: Cognitiongraph Area/Cluster: comprehension Communication 80 Boston Domain:Cognition Naming Test Area/Cluster: Communication

What is claimed is:
 1. A computer-assisted method for assessing apatient, comprising a computing device carrying out actions comprising:a. receiving an input of a first assessment of the patient in aself-care domain with items in the areas of balance, upper extremityfunction, and swallowing; b. receiving an input of a second assessmentof the patient in a mobility domain with items in the areas of balance,wheelchair skills, changing body positions, bed mobility, and mobility;c. receiving an input of a third assessment of the patient in acognition domain with items in the areas of cognition, memory, andcommunication; d. calculating a score based on the received assessments;and e. storing the calculated score in an electronic medical record. 2.The method of claim 1, further comprising: retrieving the score from theelectronic medical record; and adjusting a treatment plan based on theretrieved score.
 3. The method of claim 1, further comprising applyingfactor analysis to data derived from the electronic medical record. 4.The method of claim 1, further comprising applying classical itemanalysis data derived from the electronic medical record.
 5. A method ofmeasuring improvements in a rehabilitation patient, comprising acomputing device carrying out actions comprising: a. using item responsetheory, pre-selecting a plurality of assessments; b. receiving an inputof an assessment of the plurality relating to self-care; c. receiving aninput of an assessment of the plurality relating to mobility; d.receiving an input of an assessment of the plurality relating tocognition; and e. storing the received inputs in an electronic medicalrecord.
 6. The method of claim 5, wherein the plurality of assessmentsare further selected using factor analysis.
 7. The method of claim 6,wherein the plurality of assessments are further selected usingclassical item analysis.
 8. The method of claim 5, wherein the each ofthe self-care, mobility, and cognition assessments is broken intofactors.
 9. The method of claim 6, wherein the factors for self-care arebalance, upper extremity function, and swallowing.
 10. The method ofclaim 5, further comprising the computing device determining a score tomeasure the improvement.
 11. The method of claim 10, further comprisingthe computing device displaying the score on a display screen.
 12. Themethod of claim 10, comprising the computing device carrying out furtheractions comprising: comparing the score against a predictive score toidentify an area of therapeutic need, wherein the comparison is used toimprove a therapy.